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One of my Phonak Audeo 90’s (RIC) died the other day after 5 years and I’m shopping for new. What’s your go to hearing aid currently if you’ve upgraded recently or have been thinking of doing so?

Moderate loss, have worn them for many years, enjoy listening to music and nature, but also need help in meetings and noisy environments.

Not worried about cost and wanting to get one more good deal out of work insurance before I retire.

We’re hitting a classic distributed systems wall and I’m looking for war stories or "least worst" practices.

The Context: We maintain a distributed stateful engine (think search/analytics). The architecture is standard: a Control Plane (Coordinator) assigns data segments to Worker Nodes. The workload involves heavy use of mmap and lazy loading for large datasets.

The Incident: We had a cascading failure where the Coordinator got stuck in a loop, DDOS-ing a specific node.

The Signal: Coordinator sees Node A has significantly fewer rows (logical count) than the cluster average. It flags Node A as "underutilized."

The Action: Coordinator attempts to rebalance/load new segments onto Node A.

The Reality: Node A is actually sitting at 197GB RAM usage (near OOM). The data on it happens to be extremely wide (fat rows, huge blobs), so its logical row count is low, but physical footprint is massive.

The Loop: Node A rejects the load (or times out). The Coordinator ignores the backpressure, sees the low row count again, and retries immediately.

The Core Problem: We are trying to write a "God Equation" for our load balancer. We started with row_count, which failed. We looked at disk usage, but that doesn't correlate with RAM because of lazy loading.

Now we are staring at mmap. Because the OS manages the page cache, the application-level RSS is noisy and doesn't strictly reflect "required" memory vs "reclaimable" cache.

The Question: Attempting to enumerate every resource variable (CPU, IOPS, RSS, Disk, logical count) into a single scoring function feels like an NP-hard trap.

How do you handle placement in systems where memory usage is opaque/dynamic?

Dumb Coordinator, Smart Nodes: Should we just let the Coordinator blind-fire based on disk space, and rely 100% on the Node to return hard 429 Too Many Requests based on local pressure?

Cost Estimation: Do we try to build a synthetic "cost model" per segment (e.g., predicted memory footprint) and schedule based on credits, ignoring actual OS metrics?

Control Plane Decoupling: Separate storage balancing (disk) from query balancing (mem)?

Feels like we are reinventing the wheel. References to papers or similar architecture post-mortems appreciated.

I’m researching ideas for a new B2B product and want to understand real bottlenecks teams face.

What problems, inefficiencies, or recurring frustrations do you or your team deal with at work—where, if a solid solution existed, your company would actually pay for it?

Examples could include:

manual workflows

data or reporting pain points

communication gaps

compliance or documentation hassles

tools your team keeps hacking together internally

anything expensive, slow, or annoying

Would love to hear your role/industry (optional) and the specific problem you face.

This I did not predict to happen so quick with myself …

I work mainly in energy market communications and systems that facilitate energy trading, balancing and such. Currently most parties there take minutes to process messages and I think there could be a lot to learn from financial systems engineering. Any good resources you can recommend?

Since 5 days ago, the latency of our Finetuned 2.5 Flash models has suddenly jumped by 5x. For those less familiar, such finetuned models are often used to get close to the performance of a big model at one specific task with much less latency and cost. This means they're usually used for realtime, production use cases that see a lot of use and where you want to respond to the user quickly. Otherwise, finetuning generally isn't worth it. Many spend a few thousand dollars (at a minimum) on finetuning a model for one such task.

Five days ago, Google released Nano Banana Pro (Gemini 3.0 Image Preview) to the world. And since five days ago, the latency of our existing finetuned models has suddenly quintupled. We've talked with other startups who also make use of finetuned 2.5 Flash models, and they're seeing the exact same, even those in different regions. Obviously this has a big impact on all of our products.

From Google's side, nothing but silence, and this is talking about paid support. The reply to the initial support ticket is a request for basic information that has already been provided in that ticket or is trivially obvious. Since then, it's been more than 48 hours of nothingness.

Of course the timing could be a pure coincidence - though we've never seen any such latency instability before - but we can all see what's most likely here; Nano Banana Pro and Gemini 3 Preview consuming a huge amount of compute, and they're simply sacrificing finetuned model output for those. It's impossible to take them seriously for business use after this, who knows what they'll do next time. For all their faults, OpenAI have been a bastion of stability, despite being the most B2C-focused of all the frontier model providers. Google with Vertex claims to be all about enterprise and then breaks product of their business customers to get consumers their Ghibli images 1% faster. They've surely gotten plenty of tickets about this, and given Google's engineering, they must have automated monitoring that catches such a huge latency increase immediately. Temporary outages are understandable and happen everywhere, see AWS and Cloudflare recently, but 5+ days - if they even fix it - of 5x latency is effectively a 5+ day outage of a service.

I'm posting this mostly as a warning to other startups here to not rely on Google Vertex for user-facing model needs going forward.

Last month, a single container in my GKE cluster (Sao Paulo region) entered an error loop, outputting to stdout at ~2k logs/second. I discovered the hard way that GKE's default behavior is to ingest 100% of this into Cloud Logging with no rate limiting. My bill jumped nearly 1000% before alerts caught it.

Infrastructure (Compute): ~$140 (R$821 BRL) Cloud Logging: ~$1,300 (R$7,554 BRL)

Ratio: Logging cost 9.2x the actual servers.

https://imgur.com/jGrxnkh

I fixed the loop and paused the `_Default` sink immediately.

I opened a billing ticket requesting a "one-time courtesy adjustment" for a runaway resource—standard practice for first-time anomalies on AWS/Azure.

I have been rejected twice.

The latest response: "The team has declined the adjustment request due to our internal policies."

If you run GKE, the `_Default` sink in Log Router captures all container stdout/stderr.

There is NO DEFAULT CAP on ingestion volume which is an absurd!

A simple while(true); do echo "error"; done can bankrupt a small project.

Go to Logging -> Log Router. Edit _Default sink.

Add an exclusion filter: resource.type="k8s_container" severity=INFO (or exclude specific namespaces).

Has anyone successfully escalated a billing dispute past Tier 1 support recently?

It seems their policy is now to enforce full payment even on obvious runaway/accidental usage which is absurd since its LOGS! TEXT!

I got a fraud warning from my bank a few days ago at 7:04 PM. When I logged into my bank I found 19 pending Cursor subscription charges.

I called the Cursor billing phone number I found on my real Cursor account. It was outside of working hours so got an automated message.

I promptly fired off an email at 7:16 PM making it clear I did not authorize these purchases.

After a few days of painfully slow email responses the conclusion I am getting from them is "the compute resources are fully consumed and cannot be returned or refunded".

Anyone have advice on how to proceed?

Edit: I plan to file a dispute with my bank.

Also curious if others have experienced something similar, because clearly this is a stock "we basically won't ever refund money" response.

’ve built NeuroCode, a Python engine that builds a structural intermediate representation (IR) of codebases — including call graphs, module dependencies, and control flow — and stores it in a neural-ready format designed for LLMs.

Most tools treat code as text. NeuroCode treats it as structure. It gives you a CLI (and library) to:

Build the IR with neurocode ir . and store it as .neurocode/ir.toon

Explain files using call/import graphs: neurocode explain path/to/file.py

Run structural checks and generate LLM-ready patch plans (coming soon)

The goal is to bridge static analysis and AI reasoning. You can plug NeuroCode into agents, editors, or pipelines — or use it standalone to get structure-aware insights into your codebase.

No runtime deps, tested with Python 3.10–3.12. Still early (v0.3.0), feedback and contributions welcome.

GitHub: https://github.com/gabrielekarra/neurocode

Making a simple sandwich from ingredients is a full-time job that takes roughly 6 months. You grow chickens, fetch sea water, make bread from ingredients, and so on. Unless you sell a lot of sandwiches you made from scratch, you will bleed a lot of money and time.

Only God can make sandwich instantly. If you try to make a simple pencil, it will probably take more than 6 months.

Now, consider security and privacy. Just constructing what seems to be a reasonably private and robust linux computer took at least a year of full-time effort. It is genuinely more difficult than making a simple sandwich from ingredients, and making a simple sandwich "from ingredients" is a full-time business by itself. The so-called system crafting is a full-time business that doesn't pay.

The cost of constructing a private linux computer with your "personal" labor is your business, your job, your health, your relationships, and everything else in your life. The cost of privacy is extremely high. You need to be okay with rough edges in your computing environment.

If you force yourself to make sandwich and pencil from ingredients, make your furniture, build a house, grow foods, run an e-commerce store, construct a private linux computer, and so on, then you will not be good at any one thing, and you won't be paid much. You are only paid as much as your best expertise. Only specialization can make you rich. If you try to scatter your energy into multiple things including security and privacy, you will remain poor. Even linus torvalds, a rich computer programmer, avoids fiddling with linux kernel options on his linux computers. He just uses fedora without modification. Linus torvalds doesn't care about the fact that his AMD CPU has hardware backdoor and certainly can't be bothered to "manually" construct a backdoor-free router that blocks AMD PSP and Intel ME behind the router. But, he may "buy" computers with Intel ME disabled by others.

If you want to become rich, you should have laser focus on your core business and sacrifice other things like excellent privacy.

Now, you know what it means to sacrifice. Sacrifice may even mean you use mac pro instead of a personally hardened linux desktop. The creator of linux can't be bothered to "manually" harden his own linux computers.

If you want to be rich and have a good life, you should be ready to buy everything outside your core business. Buying things takes infinitely less time than building things from scratch.

Spending time on things outside your core business is basically a financial suicide.

Reddit has drifted over time but HN has remained a source of high signal to noise.

Just wanted to thank dang and the moderation team for making this community what it is.

Hi HN,

I built GhostBin, a lightweight pastebin designed to replace the simplicity and speed that services like ix.io used to offer. ix.io has been down for a long time, and most existing pastebins are either bloated, slow, or not CLI-friendly. I needed something minimal that “just works,” especially for piping logs and command outputs during debugging or writing content. So I made my own.

GhostBin focuses on:

Simplicity: Clean interface and a straightforward API.

Performance: Go + Redis for fast reads/writes.

CLI-first workflow: curl and shell pipelines work out of the box.

Privacy & control: Self-hostable with Docker; no vendor lock-in.

Burn-after-read + expiration: Useful for ephemeral snippets.

Optional deletion secret: Allows secure deletion via API.

Demo: https://www.youtube.com/shorts/RINJI_Q5048

Source: https://github.com/0x30c4/GhostBin

CLI script: https://raw.githubusercontent.com/0x30c4/GhostBin/main/gbin.sh

``` $ curl -F "f=@file.txt" gbin.me ```

``` dmesg | curl -F "f=@-" gbin.me ```

Hello everyone,

I’d love to hear about:

1. Tools you pay for that feel overpriced or frustrating (especially if you’d replace them immediately if something better existed), and

2. Anything that routinely costs you time, money, or attention (and how much money and time it costs you).

I’m most interested in problems that are painful enough that you’d gladly pay to fix them.

If you’re open to sharing, it'd be nice to know:

1. the exact problem 2. how you solve it now 3. the approximate budget or cost

Thank you. The more concrete and specific, the better.

My wife is a freelance writer/translator, and so many of her clients check her work with so-called AI Checkers, often making her rewrite her work until it scores below 30% (even if it was 100% her work in the first place).

If you're relying on these tools, please know that they're not accurate. At all. In fact, they're a waste of time. As an example, I pasted the first three paragraphs of the Declaration of Independence into one of these checkers, and the result: 100% AI generated via ChatGPT.

Edit: removed link to AI Checker site, as I don't want this to look like an ad. If you want to check, google "AI Checker", and test yourself.

I have had this problem for years now: Scrolling through photos on my phone that are maybe a year old or older I notice grey squares here and there in my Google Photos app (used without an account). The files are properly corrupted - can't view them on other devices either. I checked some out in a hex editor and sure enough there is a good chunk of null bytes in the beginning. Sometimes it's just the first byte and changing it from 00 to FF fixes the image. But oftentimes it's a whole lot more 00s to the point where I don't know how to recover the image.

I've been using only Pixel a-series phones for the last couple years (3a, 6a, 9a) and had this happen on all of them. It surely can't be bad storage, can it? I feel like there is a bug in some part of Google's Android OS.

Has anyone of you encountered this issue? I can't believe I'm alone with it.

Anthropic is claiming Opus 4.5 is the best frontier model for coding. Personal anecdote tests still shows Gemini 3 leading this race. What's your experience?

*`package.json` includes a `preinstall` script running `node setup_bun.js`, along with `setup_bun.js` and `bun_environment.js` files that appear to contain the malware.*

Official numbers say the U.S. isn’t in recession—does real life feel different?

Considering with AI an experienced engineer can build anything much faster and we had a discussion around "single person unicorn". How are you balancing your love for the craft and creativity. I see copycats of copycats generating decent $ per month, sometimes I wonder I should I also do the same and leave my job to pursue the unicorn dream? As every 2 years there is layoff, AI taking over... not sure if this make sense or this is just me bored on a weekend.

excluding legacy game.

I built ZetaShare because I wanted a file transfer service that doesn’t spy on me — no tracking, no data collection. The project was inspired by ToffeeShare, but after seeing it abandoned and being unable to contact the developers, I decided to build my own alternative.

In case you're curious about how the system works under the hood, here’s a simplified explanation of the current architecture:

How ZetaShare works (simplified):

The sender uploads a file on the website. The server doesn’t store the file — After uploading, the sender receives a 6‑digit ID. This ID becomes the identifier for the transfer session.

The receiver opens the link with the ?id= parameter, for example: https://zetashare.com/?id=123456

When the receiver joins the link, the server sends the receiver’s SDP offer to the sender. (This is part of the WebRTC signaling process.)

The sender responds by sending back their own SDP answer.

Both sides then exchange ICE candidates through the server.

After the WebRTC handshake is complete, a direct P2P connection is established between sender and receiver — and the file starts transferring directly between the two devices.

So the server is only used for:

exchanging SDP offer/answer

relaying ICE candidates …and nothing else. No file, no metadata, no tracking.

Once the WebRTC connection is established, all data flows peer‑to‑peer, fully outside the server.

What works now:

WebRTC P2P transfers — files go directly between users.

No metadata collection — unlike other services that send file metadata through signaling servers (like ToffeeShare), our server only handles SDP and ICE candidates.

No accounts required — just drag, drop, and share.

Unlimited file sizes.

Completely free — no ads at all.

What’s missing (in my view):

Only TLS (WebRTC’s built-in), no proper end-to-end encryption yet.

No password protection.

Links will have an expiration option soon.

Mobile file transfer speed needs improvement.

Coming next:

Password protection

File expiration option

Mobile performance improvements

Proper end-to-end encryption

Try it out: https://zetashare.com

These are the features I could think of, but if you have better ideas, I’d love to hear them. I can’t promise to build everything, but I’ll definitely try.

I’m using AI to help me because my English isn’t perfect — sorry if it sounds a bit AI-generated!

I know many of us use a really excellent PostHog service, but it seems their latest version of `posthog-js` NPM package contains malware.

Reported to their security channel, also reported to NPM, but also wanted to raise awareness here.

Update: It seems all their NPM packages have the same problem

Update 2: https://status.posthog.com/

I am doing a PhD in R1 public uni on Computer Engineering in the USA. PhD is something I really wanted to do because it allows me to dive deep into stuff. Turns out what I thought initially was not the whole story.

I am nearing the end of second year of my PhD. My advisor is in HPC but doesn't do hot research and I want to do efficient hardware AI research (faster kernels, ML systems etc). But whenever I read papers and see the authors and what they have accomplished and the team behind them and the prestigious schools/labs doing them while I am deep in some cuBLAS function hoping to optimize register tiling to be a nanosecond faster, I feel like a failure. I don't know what problems to work on. I wonder if what I want to do even requires a PhD.

I question if I will ever be able to do anything publishable as the labs in MIT/Stanford, their cohort of brilliant minds basically alone? There's so much out there that I don't know. The more I think I know the more stuffs keep popping up. I thought I had finally understood LLMs to their nitty gritty details but then there's even more new variants of attentions popping up. I am not sure if I will be able to read everything needed to be able to research. I want to be able to get a job in the US after my PhD(home is terrifying) and I am an international so quiting is not an option.

I am looking for any advice or if you were in a similar boat anything would be helpful. Thank you.

https://winterx.github.io/laser-eyes-simulator/

Needed a break from my main project, so I threw together this Laser Eyes Simulator. It's a silly little thing that adds laser beams to your eyes using your webcam. Downloadable images. Hope you enjoy!

Gemini 3 Prompt: - Use the computer's front-facing camera - Use `mediapipe` lib to capture facial landmarks - Use `threejs` to apply a LASER EYE effect to the face captured by the camera based on the real-time 3D landmark information provided by `mediapipe`

I've been meaning to set up an airgapped NetBSD 1.6 computer for playing music and writing but am unable to find any packages or source code for programs at the time. archive.netbsd.org only has packages from release 7 onwards.

I'm currently interviewing for roles in another language and it's so difficult. I'm wondering if this is universal? I'm struggling to even imagine the daily work in a company. Handling meetings, understanding requirements, standing up for my solutions... I sound like a child. Anyone lived through this? How?

Does anyone know which Android phones besides Pixel 6 and newer support CDC NCM USB tethering?

I tried few Samsung phones (S21 - S25), Xiaomi Redmi 13 and they only support RNDIS.

Also, I compiled a list of my findings, and if anyone is interested, it’s open for contributions: https://github.com/namesarehard0/android-usb-tethering

I'm tasked with building a private AI assistant for a corpus of 10 million text documents (living in PostgreSQL). The goal is semantic search and chat, with a requirement for regular incremental updates.

I'm trying to decide between:

Bleeding edge: Implementing something like LightRAG or GraphRAG.

Proven stack: Standard Hybrid Search (Weaviate/Elastic + Reranking) orchestrated by tools like Dify.

For those who have built RAG at this scale:

What is your preferred stack for 2025?

Is the complexity of Graph/LightRAG worth it over standard chunking/retrieval for this volume?

How do you handle maintenance and updates efficiently?

Looking for architectural advice and war stories.

I'm talking about greater than 5 TB in size. Rclone looks really good because I can just give it a bandwidth limit, point it at google drive and fire and forget. But I'm curious if that is the best way to do this? What does HN think?

We're a small business where we invest everything back into R&D, so we're not sitting on huge cash reserves. We have a case just filed against us that is visible online but we're waiting to be served, for patent infringement. The company claiming infringement is known to us, they have been trying to get a foothold in our market for years, but their product hasn't found solid ground. We welcome competition as it drives a healthy economy, and we've got a really positive social standing in the community for supporting our customers and we're pretty highly respected by everyone. So when we found they had filed this complaint we were shocked. The complaint has no basis in reality as our product demonstrably does not implement any method of what their patent says, it will definitely be thrown out if it goes to court. But that's a stupid waste of time and money for us both. Has anyone had any success in writing to the company directly and showing how their case will lose, so they drop it and stop spending on something that will fail? We believe they filed out of bad faith because of jealousy and frustration against us. We've taken care never to speak against them or anyone in public, we know this is just sour grapes on their part. Obviously we're going to wait until we've been served but I feel that we can avoid further costs by sending a personal de letter to the owner of this company - I feel that they also have limited funds.